Frequent pattern mining research has raised an interest in studying the dynamic behavior of patterns whose frequency significantly alters over time periods. This paper presents an investigation into using a sliding window approach in exploring the dynamic behavior of frequent patterns in consecutive time periods where they occur in a data set. This approach gives a promising alternative to the existing techniques being used for decision makers to know the exact period in which a pattern's frequency significantly alters and therefore responds appropriately. The results will help in detecting and reporting significant changes in the frequency of patterns sequentially from one time period to another.